Enlarge /. If you can't socialize yourself, a face mask will help.
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Many countries that checked their COVID-19 cases in the spring are now seeing a surge in infections, increasing the prospect of a second wave of cases, as many epidemiological models had predicted. In the United States, however, the number of cases has never fallen to low levels. Instead, it varied between high infection rates and very high peaks in cases. Why is everything so different in the States?
While there are many possible reasons, a number of new studies essentially blame all of the obvious: The United States ended social distancing rules too soon, never built up adequate testing and contact tracking skills, and failed to adopt habits like mask use that might help its Replace bugs elsewhere. The fact that some of these studies used very different methods to arrive at similar conclusions suggests that these conclusions are likely to hold up as more studies are entered.
One of the studies conducted by a US-South African team looked at the relaxation of social distancing rules in the US. The authors compiled a list of restrictions for each state and the District of Columbia, and tracked the number of COVID-19 deaths in each state for eight weeks before the rules ended. The number of deaths was used as a proxy for the total number of cases because the intermittent availability of tests made it difficult to determine the true infection rate.
Most states began easing these rules in late April. However, as the authors note, they did so without the ability to otherwise control infection. "The relaxation of such measures should be accompanied by appropriate behavioral practices (such as wearing masks and physical distancing) and control measures (such as contact tracing and increased availability of tests) so that disease control can be sustained," they write. Given the limited testing capacity and widespread disregard for behavioral practices, this simply wasn't possible.
Therefore, the authors collected data on COVID-19 deaths in states after lifting restrictions and comparing the two trajectories. Linear regression models were used to determine the number of COVID-19 deaths and to estimate the likely reproductive number for the virus in each condition and DC.
Of the 51 examples, 44 had seen the virus reproduce slowly while social boundaries existed. Overall, the authors estimate that the number of reproductions of the virus in the USA fell by an average of 0.004 per day during this period. While this wasn't dramatic, it meant that by the time they started relaxing their social distancing rules, 46 had a reproductive number less than one – a situation that would ultimately mark the end of the pandemic.
Unfortunately, that decline ended with the relaxation of the rules. After the restrictions were lifted, the estimated number of reproductions decreased from 0.004 per day to 0.013. Only eight states and DC were able to keep the reproductive number below 1.0 after the rules were relaxed, meaning the pandemic was back on track for growth.
Obviously, there are many differences between states in what restrictions were in place and the levels of infections when those restrictions were first introduced. So it's no surprise that there isn't a straightforward "before" or "after" pattern of restrictions as researchers break each of the states. However, both the overall results and the national average clearly suggest that the pandemic-targeted restrictions ended too soon.
And not enough
And if that weren't enough, an epidemiological modeling paper that focuses on a slightly different issue comes to the same conclusion. The work of a group of researchers at Texas A&M focuses on what we need to control the pandemic without returning to severe restrictions on social interaction. While we were figuring out what we would need to fight the pandemic, the A&M team figured out what these restrictions could currently do.
For the work, the researchers created a standard epidemiological model and used mobility data from companies like Google and Open Table to adjust their properties for both periods of social restriction and post-reopening. They also added country-level data on cases and deaths and validated the model using historical data.
When they actually analyzed their model, it more or less reproduced the results above. For all but five states, the virus' effective reproductive value at the start of the pandemic was less than one, "primarily during state protection on the ground". After those restrictions were lifted, the model showed that infections were starting to increase, and by mid-July 42 states and DC were likely to have virus reproduction rates that would allow the pandemic to spread.
By the last date used in their analysis – July 22nd – the chance to control the pandemic was as good as gone. Only three states, all in the northeast, could control it without adding additional social restrictions. Absolutely no one would be able to do this if they relaxed existing boundaries. Even if states doubled existing testing and contrast tracking, just eight could cut virus replication rates enough to bring the pandemic under control. Another 30 would have to do so and increase social restrictions. The rest would have to get into a heavy suspension again.
"We have shown that in most states, control strategies implemented during their protection period were sufficient to contain the outbreak," the authors conclude. "For the majority of states, however, our modeling suggests that the reopening came too quickly and / or without adequate testing and contact tracing to prevent the epidemic from recurring."
I'm already wearing a mask
The authors acknowledge that their model has a notable weakness: it assumes that personal protective measures such as face mask use and physical distancing are roughly proportional to the number of people who obey state-mandated social restrictions. This is not an unreasonable assumption, but it does prevent the model from analyzing the effects of these personal measures separately from the official guidelines for limiting social contacts.
That brings us to a draft paper that has not yet been peer reviewed but addresses the problem directly using data from Ontario, Canada. The authors compared infection rates in 34 different public health counties in Ontario before and after mask mandates were passed. Like the A&M group, the authors use Google mobility data to control the frequency of personal interactions. Overall, they estimate that mask use likely reduced the infection rate in Ontario by 20 to 40 percent.
None of this should come as a surprise. From the beginning, public health officials said the social restrictions were needed to control the rate of infection so testing and contact tracing could effectively keep the pandemic at bay. Data from the pandemic only showed that initial advice was spot on. The United States' response, however, was to lift the restrictions before the infection rate was controlled and restrict the testing enough to make contact tracing nearly impossible. As an added bonus, the country has made some of the possible alternative ways to contain the pandemic, such as the use of protective masks, a political issue.
While the newspapers give us an indication of what will be necessary to keep the United States from spreading the pandemic further out of control, they also show how we did pretty much everything wrong.